This project introduces a novel approach for generating synthetic data using generative AI (GenAI) to provide more accurate evaluations of existing and new quantitative methods in real-world settings. Our framework consists of five key steps: (i) pre-processing input data, (ii) training GenAI models on input data, (iii) assessing synthetic data quality, (iv) conducting AI-based simulations, and (v) evaluating simulation results. Our original work on this project can be found below.
Publications/Working Papers
- Suk, Y., Pan, C., & Yang, K. (2025). Using Generative AI for sequential data generation in Monte Carlo simulation studies. PsyArXiv. [Preprint]
Recent Conferences/Seminars
- Suk, Y., & Yang, K. (2024, July). Using Conditional Tabular Generative Adversarial Networks for process data generation in Monte Carlo simulation studies. The International Meeting of Psychometric Society (IMPS), Prague, Czech Republic.